Saradindu Sengupta
I am working at Nunam, an energy analytics startup based in Bangalore, India, where my primary area of work is building health and lifecycle forecasting of Li-ion batteries in EV and energy storage. I have over 4 years of professional experience in building ML systems from the ground up after finishing my master's from IIITM, Kerala. I have spoken at both physical and virtual conferences where my primary area of focus has been on Computer Vision, MLOps, model interpretability and model compression and quantization.
Previous Talks
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"Managing data quality issues in ML production, especially for time-series" - Link Slides at Google Developer Group Community Day, 2022
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"Things I learned while running neural networks on microcontroller" - Link Slides at PyData Global 2022
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"Bessel's Correction: Effects of (n-1) as the denominator in Standard deviation" - Link Slides at PyData Global 2022
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"Interpretable ML in production" - [Slides](https://docs.google.com/presentation
Sessions
In the recent past with the explosion of large language or vision models, it became inherently very costly to train models on new data. Coupled with that the various new data privacy legislations introduced or to be introduced make the "right to be forgotten" very costly and time-consuming. In this talk, we will go through the current state of research on "machine unlearning", how a learnt model forgets something without retraining and a general demonstration of the machine unlearning framework.